What are the Indices of Deprivation, and how are they useful?
The Indices of Multiple Deprivation (IMD) are a set of datasets used in the UK to measure and rank the relative deprivation of small geographic areas. The IMD is the official measure of relative deprivation for small areas (or neighbourhoods); England, Wales and Scotland have their own versions of the IMD. In England, the IMD ranks every small area from 1 (most deprived area) to 32,844 (least deprived area). It is common to describe how relatively deprived a small area is by saying whether it falls among the most deprived 10 per cent, 20 per cent or 30 per cent of small areas in England (although there is no definitive cut-off at which an area is described as ‘deprived’). The IMD is a relative measure, meaning it identifies areas that are more deprived than others, but it doesn't indicate the exact degree of deprivation.
“Therefore, it is important that when thinking about data and its application to health we are not mixing apples and oranges; data intentionally created for one purpose may not mean it’s applicable for another. Part of the Data for Health Justice movement is to recognise that data intentionally used for health justice considers the human, societal factors when creating ranges and values.”
There are seven distinct domains of deprivation which, when combined and appropriately weighted, form the IMD. They are; - Income (22.5%) - Employment (22.5%) - Health Deprivation and Disability (13.5%) - Education, Skills Training (13.5%) - Crime (9.3%) - Barriers to Housing and Services (9.3%) - Living Environment (9.3%).
What do people use the Index of Multiple Deprivation for?
Organisations use the Index of Multiple Deprivation, sometimes in conjunction with other data, to distribute funding or target resources to areas. An example of this is how Centric Lab has used the IMD alongside environmental stressor data to create the Biological Inequities Index. This process allowed us to identify hotspots in the UK that presented a risk to health and present findings to a range of organisations.
The IMD is used by government authorities to ensure programmes support the most deprived areas. Local and regional governments use the IMD as evidence in the development of strategies, to target interventions, and in bids for funding.
How useful is it?
The IMD is very useful in helping articulate through data how certain areas are unbalanced in relation to others on a specific set of criteria. However, there are limitations to the data and its use.
Firstly, the data’s indicators are broad and whilst a lot of work has gone into them as indicators of deprivation they may not reflect everyone’s views on what deprivation is. The data cannot also speak alone about the people in these areas and their perceptions and experiences of deprivation. For example, within relatively deprived areas it is possible to find areas of high social capital which is seen as a positive indicators towards health and wellbeing. Equally, many communities may not see value in certain services and feel that if installed they may unsettle a harmony that exists. Therefore, the data should be used as an information point rather than a decision making tool.
Secondly, the data can be used to unintentionally enact harm. If the opposite of deprivation of assets, services, and resources is abundance then the means to “level up” could create further problems. For example, the development of assets, services, and resources is typically seen through an economic lens of investment, revenue generation and sustainability. When this process is delivered through an extractive economic method then greater harm could be being caused to the community as opposed to other solutions; if you only look at levelling up deprivation through an economic lens then you’ll only entrench inequities caused by economics. These inequities can occur when there are loose employment laws protecting rights of low income earners, or they can be when investment funds are directed towards businesses situated outside of the area rather than focusing on community wealth building.
Therefore, it is important that when thinking about data and its application to health we are not mixing apples and oranges; data intentionally created for one purpose may not mean it’s applicable for another. Part of the Data for Health Justice movement is to recognise that data intentionally used for health justice considers the human, societal factors when creating ranges and values.
Using data such as the IMD can be useful as a starting point for engagement or project framing. However, as a starting point it should be there to frame conversations around health and what solutions communities envisage (or building on what is already being done). You may find that communities would prefer support on mutual aid projects rather than a brand new health centre that takes 10 years to build at great expense over which they may have little oversight or input.
Data is just a collection of indicators with values prescribed to them by someone. Data doesn’t tell the story of people.